I have a rather difficult database to work with: 
The code variable is 141 different. What I wanted to do is create a data.frame that had code as a variable (header) and so had a sequence of days in a row for each code. A column would also be the date of days followed day. But I have to take into account the fact that a few months do not have day 31, for example. I have no idea how to do it.
Any suggestions?
Well, I'll try to give you an answer with a generic example, similar to the data you used. What you will do first is to pass your data to the format "long", that is, you will transform the columns "day1", "day2" etc into a single categorical variable:
Creating a sample data.frame:
exemplo <- data.frame(code = letters,
day1 = rnorm(length(letters)),
day2 = rnorm(length(letters)),
day3 = rnorm(length(letters)))
head(exemplo)
code day1 day2 day3
1 a -0.3851659 -0.07490578 0.38106308
2 b 4.1452620 -0.24116622 -0.40116849
3 c -0.1889001 -1.51602212 0.88170295
4 d 0.9936035 -0.44319328 -0.01682136
5 e 0.4648850 -0.58219348 -0.41819945
6 f 1.7426216 0.98757578 -0.07354605
Now let's pass this data to the "long" format. We can do this with the melt() function of the reshape2 package:
library(reshape2)
exemplo_melt <- melt(exemplo, id.vars = "code")
head(exemplo_melt)
code variable value
1 a day1 -0.3851659
2 b day1 4.1452620
3 c day1 -0.1889001
4 d day1 0.9936035
5 e day1 0.4648850
6 f day1 1.7426216
Now we can pass the data.frame back to the "wide" format, using the dacast() function. But we'll do this by putting the days in the rows and the codes in the columns:
dcast(exemplo_melt, variable~code)
variable a b c d e f g h
1 day1 -0.38516591 4.1452620 -0.1889001 0.99360350 0.4648850 1.74262161 -1.6665135 -0.8941499
2 day2 -0.07490578 -0.2411662 -1.5160221 -0.44319328 -0.5821935 0.98757578 -2.0520208 0.4832304
3 day3 0.38106308 -0.4011685 0.8817030 -0.01682136 -0.4181995 -0.07354605 0.9177027 0.5630105
i j k l m n o p q
1 0.2417189 0.1431401 0.1194610 -0.8504266 -0.7160816 2.3065825 0.8264834 0.42561860 -1.0942050
2 -0.9602065 -1.4234463 1.7000449 -0.3513610 0.9081842 0.9250270 1.6155795 1.10958103 -0.7349258
3 -0.7072439 3.1664578 0.1780183 -0.8852057 2.1582402 0.7957866 0.8672011 -0.09192443 -0.8870918
r s t u v w x y z
1 -1.4388341 0.2578094 0.3369311 -0.09216785 1.742366 -0.1497611 0.1625464 1.8062656 0.7381403
2 1.2544363 1.0260923 -0.6567604 -2.47889862 -1.120961 -0.2914160 -0.6510242 -0.4099567 2.2478873
3 -0.5669763 0.1865534 -0.9974459 0.40675483 2.093616 0.4234626 0.2708611 1.8284166 -0.6641648
I have worked with similar data. Perhaps the fread package: data.table might help you. It makes reading the data extremely fast!